Intel Health & Life Sciences | Make it Personal

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Today: Many disparate data types, streams…

Genomics/Analytics

Genomics

Clinical

Claims & transactions

Meds & labs

Patient experience

Personal data

Big Data is the Foundation of Precision Medicine

Future: Integrated computing and integrated data

Leading to better decisions

Improved patient experience

Healthier population outcomes

Reduced costs

Accelerate transition to personalized medicine

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Analytics in action:

Penn Medicine

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OBJECTIVE

Predict heart failure patients who are at risk of hospital re-admission within 30 or 90 days of discharge

CHALLENGE

Analyze large amounts of unstructured data in patient records across multiple hospitals in a network

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Predicting Heart Failure with Machine Learning

42,358 Raw MedicationsFrom EMRs

allopurinol, clindamycin, coumadin,

dextrose, docusate, fluconazo, gabapentin,

glargine, heparin, hydrocortisone, insulin,

lansoprazo, lantus, levothroid,

levothyroxine, lovenox, morphine,

neurontin, omeprazo, oxycodone,

pneumococcal, senna, sertraline,

subcutaneous, testosterone, therapy, valp, warfarin, zolof …….

23,663Standardized

Medication Names

Pain Management, Heart Disease,

Diabetes, Liver Failure, Respiratory, ……

20 Derived Indicators

Apply text processing & regular expressions

Apply “LDA” machine learning

More At-risk Patients, Identified Early On, Enables Better Care

Build Model of Indicators Predict Individual Risk Using Indicators & Machine Learning

0,9

0,95

1

1,05

1,1

1,15

1,2

Patient E H R Only

(baseline)

E H R + Meds

Before Admit

E H R + Meds

Before Discharge

15% Relative Predictive

Model Performance Improvement

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demo— —

demo

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Intel-led open source project

Accelerates the collaborative creation of cloud-native applications driven by Big Data Analytics

Eases the development of analytic models by data scientists and their use by developers

Optimized for performance and security

Trusted Analytics Platform (TAP)

Powers the journey from data’s potential to value www.trustedanalytics.org

Intel Health & Life Sciences | Make it Personal Intel Confidential – Do Not ForwardFrom 12 WGS in 35 hours, to 96 WGS in 11 hours

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Intel Collaborative Cancer Cloud (CCC)

Q

Q

Lab #2

OHSU

Lab #1

Learn more about our work with OHSU:OHSU’s ExacloudCollaborative Analytics for Personalized Cancer Care

Learn more about precision medicine and genomic research:www.intel.com/healthcare/optimizecodehttps://www.whitehouse.gov/precision-medicine

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Big data Unicancer ConSoRe: analysis of clinical records for cancer care

*Other names and brands may be claimed as the property of others.

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Query the UNICANCER EMRs, as they’ve accumulated records over many years with

extensive doctor annotations.

Use natural language processing and the power of big data analytics for this purpose.

Propose a user-friendly interface for physicians.

Add potential other sources of data (next steps).

Obtain instant cohorts of real patients allowing better decisions and easier clinical trial

recruitment.

By definition, there is not very much literature on rare diseases.

With precision medicine, many more rare diseases will be discovered.

Actual patient cases are few, and clinical trials must be built over various healthcare sites. This takes time and is

costly.

Rare cancers demand rapid treatment decisions and cannot wait for lengthy clinical trials.

Fast response to media health threats is difficult.

SolutionGeneral challenge

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ConSoRe: using natural language processing to identify patients for clinical trials

*Other names and brands may be claimed as the property of others.

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We recently had a metastatic breast cancer research project where it took

30 people reviewing patient records for six months to assemble a cohort of

patients who had been treated in one of the 20 French cancer centers. We believe ConSoRe will help us do that

within a matter of hours or days.Pierre Heudel

Oncologist

Centre Léon Bérard

Query the UNICANCER EMRs.

Use natural language processing and the power of big data analytics.

Obtain instant cohorts of real patients allowing better decisions and better response to media

health threats.

32% of trial costs are attributed to recruiting participants